AVP- IT – Agentic AI

Job PurposeDrive the identification and delivery of innovative technology solutions that address business challenges and growth opportunities across RMZ entities. Collaborate with Function Heads and key stakeholders to deeply understand business priorities and translate them into a clear, actionable technology product roadmap.Lead end-to-end product lifecycle management for AI and advanced technology initiatives, from requirements gathering and solution design to delivery and impact measurement, ensuring alignment with business goals.Champion adoption of AI-driven products through education, pilot programs, and continuous improvements based on user feedback and performance data.Provide strategic consulting to senior leadership by leveraging deep domain expertise and emerging technology trends to create next-generation solutions that enable business transformation.Foster a culture of innovation and knowledge sharing, enhancing team capabilities and maintaining up-to-date expertise on industry advancements.This focus emphasizes product leadership, strategic direction, and innovation without positioning liaisoning as a primary responsibility.Core Skills, Knowledge & Competencies: ü AI & ML Literacy: Ability to leverage core AI/ML concepts, limitations, and value to technical and business stakeholders; prompt engineering familiarity.ü Multi-Agentic Framework: Know-how of latest agentic frameworks like MS Agents Framework, OpenAI’s Framework, Crew AI.ü Strategic design and orchestration of agent-based systems, where autonomous agents can execute complex workflows and decision-making.ü Retrieval-Augmented Generation: Expertise in integrating RAG pipelines that connect large language models to proprietary and real-time external data, optimizing accuracy and contextual relevance.ü Data Strategy & Context Engineering: Blueprinting relevant data flows for product intelligence, ensuring effective context window management for AI models.ü Rapid Prototyping: Use of low-code, no-code, and open-source frameworks for swift prototype builds and experimentation (e.g., LangChain, LangGraph, CrewAI, Lindy)ü Multi-Agent System Design: Understanding protocols for agent communication, escalation, and error handling; deployment of resilient agentic architectures.ü Product Strategy for AI: Roadmapping features focused on trust, accuracy, autonomous action, and business impact.ü User Experience in AI Products: Ensuring intuitiveness and transparency, interpreting user signals to iteratively improve agent performance.ü Data & Ethical Governance: Knowledge of data privacy, bias, synthetic data generation, and responsible AI practices.ü AI Evaluation: Setting, monitoring, and communicating success criteria for RAG and agent-based products (accuracy, business KPIs, user acceptance)ü Exposure to Capital Markets, Construction Management, would be added advantageü Cross-Functional Collaboration: Bridging the gap between data/AI engineers, designers, business leaders, and compliance/legal stakeholders.ü Technical Communication: Translating complex agentic and AI concepts into clear, actionable product requirements, KPIs, and roadmaps.ü Change Management: Leading teams through AI adoption and process transformation, defining agentic automation opportunities in legacy workflows.ü Continuous Learning: Staying current with emerging agent frameworks, proprietary RAG solutions, and novel AI-driven product approaches.ü Impact Measurement: Using analytics to track AI product usage, agent impact, trustworthiness, and user feedback